Systems and methods for selecting an intervention based on effective age

ABSTRACT

A system for selecting an intervention based on effective age includes configured determine a user endocrinal age factor using at least a measure of user endocrine function, to determine a user telomeric age factor using a user telomere length, determine a user negative habit factor, and to multiply each factor by a user chronological age to obtain a user effective age. The at least a server is configured to derive a user health quality vector listing user priorities including life-expectancy increase. The at least a server is configured to generate a plurality of interventions, each with a vector having similar entries to the health quality vector. The at least a server is configured to select an intervention from the plurality of interventions by generating a loss function of the plurality of interventions and the user health quality vector and minimizing the loss function.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tosystems and methods for selecting an intervention based on effectiveage.

BACKGROUND

Analysis and recommendation generation regarding longevity is currentlyfraught with imprecision, due to the multiplicity of factors involved.This is further complicated by a lack of quantitative measuresindicative of implementation of solutions; statistical soundness of amodel is only predictive inasmuch as it reflects genuine feasibility ofaggregated outputs.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for selecting an intervention based on effectiveage includes at least a server. The at least a server is designed andconfigured to record a user blood test indicating at least a measure ofuser endocrine function. The at least a server is designed andconfigured to determine a user endocrinal age factor using the at leasta measure of user endocrine function. The at least a server is designedand configured to record a user genetic sample, wherein the user geneticsample includes a measure of user telomere length. The at least a serveris designed and configured to determine a user telomeric age factorusing the user telomere length. The at least a server is designed andconfigured to determine a user negative habit factor. The at least aserver is designed and configured to calculate at least a user effectiveage, wherein calculating the at least a user effective age furthercomprises multiplying a user chronological age by the user telomeric agefactor, the user endocrinal age factor, and the user negative habitfactor. The at least a server is designed and configured to derive auser health quality vector, wherein the user health quality vectorfurther comprises a plurality of health vector entries including aneffective age reduction value indicating a degree of importance ofeffective age reduction and at least a life quality objective valueindicating a numerical measure of a user life quality priority. The atleast a server is designed and configured to generate a plurality ofinterventions, wherein each intervention of the plurality ofinterventions includes an intervention vector having a plurality ofintervention vector entries, the plurality of intervention vectorentries includes a vector entry corresponding to each health vectorentry of the plurality of health vector entries, and each interventionvector entry indicates a degree of impact on a factor represented by ahealth vector entry. The at least a server is designed and configured toselect an intervention from the plurality of interventions, whereinselecting the intervention further comprises generating a loss functionof the plurality of interventions and the user health quality vector,minimizing the loss function, and selecting the intervention from theplurality of interventions as a function of minimizing the lossfunction.

In another aspect, a method of selecting an intervention based oneffective age includes recording, by at least a server, a user bloodtest indicating at least a measure of user endocrine function,determining, by the at least a server, a user endocrinal age factorusing the at least a measure of user endocrine function, recording, bythe at least a server, a user genetic sample, wherein the user geneticsample includes a measure of user telomere length, determining, by theat least a server, a user telomeric age factor using the user telomerelength, determining, by the at least a server, a user negative habitfactor, calculating, by the at least a server, at least a user effectiveage, wherein calculating the at least a user effective age furtherincludes multiplying a user chronological age by the user telomeric agefactor, the user endocrinal age factor, and the user negative habitfactor, deriving, by the at least a server, a user health qualityvector, wherein the user health quality vector further includes aplurality of health vector entries including an effective age reductionvalue indicating a degree of importance of effective age reduction andat least a life quality objective value indicating a numerical measureof a user life quality priority, generating, by the at least a server, aplurality of interventions, wherein each intervention of the pluralityof interventions includes an intervention vector having a plurality ofintervention vector entries, the plurality of intervention vectorentries includes a vector entry corresponding to each health vectorentry of the plurality of health vector entries, and, each interventionvector entry indicates a degree of impact on a factor represented by ahealth vector entry, and selecting, by the at least a server, anintervention from the plurality of interventions, wherein selecting theintervention further includes generating a loss function of theplurality of interventions and the user health quality vector,minimizing the loss function, and selecting an intervention from theplurality of interventions as a function of minimizing the lossfunction.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for selecting an intervention based on effective age;

FIG. 2 is a block diagram illustrating an exemplary embodiment of a userdatabase;

FIG. 3 is a block diagram illustrating an exemplary embodiment of anexpert database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of anintervention element database;

FIG. 5 is a flow diagram illustrating an exemplary embodiment of amethod of selecting an intervention based on effective age; and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Embodiments disclosed herein use a combination of machine learningprocesses to determine an effective age of a person based on physicallyextracted samples. A vector weighting various objectives as derived byfurther processes is used to match one or more potential interventionsfor improvement of effective age by minimizing a loss function to find abest-match solution. Classification of data to endocrinal life phasesmay be used to limit training data to closely matched cohorts.

Referring now to FIG. 1, an exemplary embodiment of a system 100 forselecting an intervention based on effective age is illustrated. System100 includes at least a server 104. At least a server 104 may includeany computing device as described in this disclosure, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described in this disclosure.Computing device may include, be included in, and/or communicate with amobile device such as a mobile telephone or smartphone. At least aserver 104 may include a single computing device operatingindependently, or may include two or more computing device operating inconcert, in parallel, sequentially or the like; two or more computingdevices may be included together in a single computing device or in twoor more computing devices. At least a server 104 may interact with oneor more additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting at least a server 104 to one or more of a variety ofnetworks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. At least a server 104 mayinclude but is not limited to, for example, a computing device orcluster of computing devices in a first location and a second computingdevice or cluster of computing devices in a second location. At least aserver 104 may include one or more computing devices dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. At least a server 104 may distribute one or more computing tasksas described below across a plurality of computing devices of computingdevice, which may operate in parallel, in series, redundantly, or in anyother manner used for distribution of tasks or memory between computingdevices. At least a server 104 may be implemented using a “sharednothing” architecture in which data is cached at the worker, in anembodiment, this may enable scalability of system 100 and/or computingdevice.

With continued reference to FIG. 1, at least a server 104 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, at least aserver 104 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. At least a server 104 may perform any step or sequence of stepsas described in this disclosure in parallel, such as simultaneouslyand/or substantially simultaneously performing a step two or more timesusing two or more parallel threads, processor cores, or the like;division of tasks between parallel threads and/or processes may beperformed according to any protocol suitable for division of tasksbetween iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 1, at least a server 104 may be configured torecord a user blood test indicating at least a measure of user endocrinefunction. Recording a user blood test, as used herein, may includereceiving or extracting data from a user blood test. User blood test maybe extracted and/or analyzed by a blood sample extractor 108, which maybe integrated in at least a server 104, connected to at least a server104, in communication with at least a server 104, and/or may operateindependently of at least a server 104; data acquired and/or extractedby blood sample extractor 108 may be transmitted or otherwise providedto at least a server 104 using any suitable process for transfer ofelectronic data and/or memory. As a further non-limiting example, aperson may enter data into a device connected to and/or communicatingwith at least a server 104, describing one or more user blood testelements and/or results; person may be a medical professional, definedfor the purposes of this disclosure as a person that performs any phaseof medical diagnosis and/or treatment, including without limitation adoctor, nurse, medical technician, diagnostic test technician, labtechnician, or the like.

Continuing to refer to FIG. 1, user blood test indicates at least ameasure of endocrine function, where “at least a measure of endocrinefunction,” as used in this disclosure, is at least a diagnostic datumindicating a state of health of a user's endocrine system. At least ameasure of endocrine function may include at least an endocrine level,where an “endocrine level” is defined as an amount of a hormone asdetected in a blood sample. An endocrine level may include, withoutlimitation, a level of estrogen, testosterone, human growth hormone,mcaon, aldosterone, calcitonin, renin, prolactin, follicle-stimulatinghormone, luteinizing hormone, norepinephrine, epinephrine, parathyroidhormone, cortisol, insulin, thyroid hormones, cholesterol,dehydroepiandrosterone (DHEAS), DHEA-Sulfate, insulin-like growth factor1 (IGF-1), adipokines such as adiponectin, leptin, and/or ghrelin,somatostatin, gonadotropin-releasing hormone (GnRH) and/or progesterone,as well as ratios and/or relative quantities of endocrine levels, suchas without limitation a ratio of DHEAS to cortisol. Measures ofendocrinal function may include a change in at least an endocrinallevel; for instance, user may have started with a given level of ahormone or other endocrinal chemical, which may have been determinedusing a previous blood sample, and which may have changed prior to theextraction of an instant blood sample. This may be recorded in anysuitable data form, including as an absolute change, a relative change,a percentage, or the like.

Still referring to FIG. 1, recording user blood test may includerecording and/or receiving additional data that may be extracted fromblood tests, including without limitation toxicology data, includingdata indicative of chemical contamination, levels of chemicalsconsistent with drug use, or the like. Additional data may include dataindicative of damage to one or more organs such as without limitationliver damage, kidney damage, or the like. Additional blood sample datamay include hematological data, such as red blood cell count, which mayinclude a total number of red blood cells in a person's blood and/or ina blood sample, hemoglobin levels, hematocrit representing a percentageof blood in a person and/or sample that is composed of red blood cells,mean corpuscular volume, which may be an estimate of the average redblood cell size, mean corpuscular hemoglobin, which may measure averageweight of hemoglobin per red blood cell, mean corpuscular hemoglobinconcentration, which may measure an average concentration of hemoglobinin red blood cells, platelet count, mean platelet volume which maymeasure the average size of platelets, red blood cell distributionwidth, which measures variation in red blood cell size, absoluteneutrophils, which measures the number of neutrophil white blood cells,absolute quantities of lymphocytes such as B-cells, T-cells, NaturalKiller Cells, and the like, absolute numbers of monocytes includingmacrophage precursors, absolute numbers of eosinophils, and/or absolutecounts of basophils. Additional blood sample data may include datadescribing blood-born lipids, including total cholesterol levels,high-density lipoprotein (HDL) cholesterol levels, low-densitylipoprotein (LDL) cholesterol levels, very low-density lipoprotein(VLDL) cholesterol levels, levels of triglycerides, and/or any otherquantity of any blood-born lipid or lipid-containing substance.Additional blood sample data may include data of glucose metabolism suchas fasting glucose levels and/or hemoglobin A1-C(HbA1c) levels.Additional blood sample data may include may include quantities ofC-reactive protein, estradiol, ferritin, folate, homocysteine,prostate-specific Ag, thyroid-stimulating hormone, vitamin D, 25hydroxy, blood urea nitrogen, creatinine, sodium, potassium, chloride,carbon dioxide, uric acid, albumin, globulin, calcium, phosphorus,alkaline photophatase, alanine amino transferase, aspartate aminotransferase, lactate dehydrogenase (LDH), bilirubin, gamma-glutamyltransferase (GGT), iron, and/or total iron binding capacity (TIBC), orthe like.

Still referring to FIG. 1, at least a measure of user endocrine functionmay be stored in a user database 112. User database 112 may include anydata structure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module. A user database 112 may beimplemented, without limitation, as a relational database, a key-valueretrieval datastore such as a NOSQL database, or any other format orstructure for use as a datastore that a person skilled in the art wouldrecognize as suitable upon review of the entirety of this disclosure. Auser database 112 may include a plurality of data entries and/or recordscorresponding to user tests as described above. Data entries in a userdatabase 112 may be flagged with or linked to one or more additionalelements of information, which may be reflected in data entry cellsand/or in linked tables such as tables related by one or more indices ina relational database. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which dataentries in a user database 112 may reflect categories, cohorts, and/orpopulations of data consistently with this disclosure.

With continued reference to FIG. 1, at least a server 104 is configuredto determine a user endocrinal age factor using the at least a measureof user endocrine function. A “user endocrinal age factor,” as used inthis disclosure, is a factor that may be multiplied by a user'schronological age to reflect an effect that endocrinal data has on theuser's effective age. A user's “chronological age,” as defined in thisdisclosure, is an age of the user as measured in years, or other unitsof time, from the date of the user's birth to the date of themeasurement, where a “date” may include any calendar date, Julian date,or the like. A chronological age may be used to project a user's“actuarial life expectancy,” defined as a probable age of death, aspredicted using any actuarial method and/or table, and/or an intervalfrom a date such as the present date to the probable age of death;actuarial methods may include looking up and/or calculating a user'slife expectancy using date of birth and/or demographic information aboutthe user such as sex, ethnicity, geographic location, nationality, orthe like. A “user effective age,” as used in this disclosure, is an ageof a user as adjusted to reflect a life expectancy that differs from anactuarially projected life expectancy. For instance, a user effectiveage of a person predicted to life fewer years than actuarially projectedmay be higher than a user effective age of a person predicted to matchand/or exceed an actuarially projected life expectancy. User effectiveage may be used as a representation of a user's likely overall state ofhealth, inasmuch as a user's likelihood to exceed or fall short ofactuarially projected life expectancy may be closely linked to a user'sstate of health.

Still referring to FIG. 1, calculation may include prediction of avariance from actuarial life expectancy for a given person, where a“variance from actuarial life expectancy” is a difference between anactuarial life expectancy for that person and a projected number ofyears until death as determined based on the at least a measure of userendocrine function. A difference between these two values may be addedto a user chronological age and then divided by the user chronologicalage to calculate a “raw” factor, which may represent an estimated effectof endocrinal measures on life expectancy without regard for relatednessto other variables; a raw factor may then be multiplied by a weight todetermine the endocrinal age factor, where the weight may account forinterrelatedness between endocrinal measures and other measures used tocalculate user effective age as described herein. Processes fordetermination and/or calculation of weights for this purpose may beperformed as described in further detail below.

With continued reference to FIG. 1, at least a server 104 may determinea user endocrinal age factor using one or more endocrine levels and/orchanges in endocrine levels as described above. For instance, andwithout limitation, a preadolescent child may experience increase and/ordecreases in growth hormone which may be consistent with differentstages in childhood development and growth. As a further non-limitingexample, various stages of adolescence may similarly be associated withincreases in GnRH, luteinizing hormone, follicle-stimulating hormone,testosterone (which may particularly increase in boys), and/or estrogen(which may particularly increase in girls). As an additionalnon-limiting example, various points and/or phases of an aging processmay be associated with decreases in hormones such as without limitationdecreases in estrogen, for instance in women, decreases in testosterone,for instance in men, decreases in growth hormone, decreases inmelatonin, decreases in aldosterone, deceases in calcitonin, decreasesin renin, and/or decreases in prolactin. As a further non-limitingexample, various points and/or phases of an aging process may beassociated with increases in hormones, such as without limitationincreases in follicle-stimulating hormone, increases in luteinizinghormone, increases in norepinephrine, increases in parathyroid hormone,and/or increases in epinephrine, for instance in persons with extremelyadvanced age.

Continuing to refer to FIG. 1, at least a server 104 may determine userendocrinal age factor by retrieving user endocrinal age factor from anexpert database 116. Expert database 116 may be implemented in any waysuitable for implementation of user database 112 as described above.Expert submissions may be provided in any suitable manner, includingusing one or more entries on a user interface provided on an expertclient device or the like, for instance according to embodiments asdescribed in further detail below.

Alternatively or additionally, and still referring to FIG. 1, at least aserver 104 may determine a user endocrinal age factor using one or moremachine-learning and/or deep learning processes. A machine learningprocess is a process that automatedly uses a body of data known as“training data” and/or a “training set” to generate an algorithm thatwill be performed by a computing device/module to produce outputs givendata provided as inputs; this is in contrast to a non-machine learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language.

With continued reference to FIG. 1, at least a server 104 may performmachine learning tasks as described herein using regression algorithmsand/or models, including without limitation linear regression algorithmsand/or models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

Still referring to FIG. 1, machine-learning algorithms used and/orimplemented by at least a server 104 may include, without limitation,linear discriminant analysis. Machine-learning algorithms may includequadratic discriminate analysis. Machine-learning algorithms may includekernel ridge regression. Machine-learning algorithms may include supportvector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest-neighborsalgorithms including without limitation K-nearest neighbors algorithms.Machine-learning algorithms may include Gaussian processes such asGaussian Process Regression. Machine-learning algorithms may includecross-decomposition algorithms, including partial least squares and/orcanonical correlation analysis. Machine-learning algorithms may includenaïve Bayes methods. Machine-learning algorithms may include algorithmsbased on decision trees, such as decision tree classification orregression algorithms. Machine-learning algorithms may include ensemblemethods such as bagging meta-estimator, forest of randomized tress,AdaBoost, gradient tree boosting, and/or voting classifier methods.Machine-learning algorithms may include neural net algorithms, includingconvolutional neural net processes.

With continued reference to FIG. 1, machine-learning algorithms mayinclude supervised machine-learning algorithms. Supervised machinelearning algorithms, as defined herein, include algorithms that receivea training set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm mayinclude sensor data and/or data produced via analysis as described aboveas inputs, degrees of risk and/or degrees of driver inattentiveness asoutputs, and a scoring function representing a desired form ofrelationship to be detected between inputs and outputs; scoring functionmay, for instance, seek to maximize the probability that a given inputand/or combination of elements inputs is associated with a given outputto minimize the probability that a given input is not associated with agiven output. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in training data. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various possible variations of supervised machine learningalgorithms that may be used to determine relation between inputs andoutputs.

Still referring to FIG. 1, supervised machine-learning processes mayinclude classification algorithms, defined as processes whereby acomputing device derives, from training data, a model for sorting inputsinto categories or bins of data. Classification may be performed using,without limitation, linear classifiers such as without limitationlogistic regression and/or naïve Bayes classifiers, nearest neighborclassifiers, support vector machines, decision trees, boosted trees,random forest classifiers, and/or neural network-based classifiers.

With continued reference to FIG. 1, machine learning processes mayinclude unsupervised processes. An unsupervised machine-learningprocess, as used herein, is a process that derives inferences indatasets without regard to labels; as a result, an unsupervisedmachine-learning process may be free to discover any structure,relationship, and/or correlation provided in the data. Unsupervisedprocesses may not require a response variable; unsupervised processesmay be used to find interesting patterns and/or inferences betweenvariables, to determine a degree of correlation between two or morevariables, or the like.

Still referring to FIG. 1, machine-learning processes as described inthis disclosure may be used to generate machine-learning models. Amachine-learning model, as used herein, is a mathematical representationof a relationship between inputs and outputs, as generated using anymachine-learning process including without limitation any process asdescribed above, and stored in memory; an input is submitted to amachine-learning model once created, which generates an output based onthe relationship that was derived. For instance, and without limitation,a linear regression model, generated using a linear regressionalgorithm, may compute a linear combination of input data usingcoefficients derived during machine-learning processes to calculate anoutput datum. As a further non-limiting example, a machine-learningmodel may be generated by creating an artificial neural network, such asa convolutional neural network comprising an input layer of nodes, oneor more intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning, forinstance for multi-layered networks.

Alternatively or additionally, and still referring to FIG. 1,machine-learning may be performed without creating models, for instancevia a lazy-learning process. A lazy-learning process and/or protocol,which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements. Lazy learning may implement any suitable lazylearning algorithm, including without limitation a K-nearest neighborsalgorithm, a lazy naïve Bayes algorithm, or the like; persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various lazy-learning algorithms that may be applied to generateoutputs as described in this disclosure, including without limitationlazy learning applications of machine-learning algorithms as describedin further detail below.

With continued reference to FIG. 1, training data, as used herein, isdata containing correlation that a machine-learning process may use tomodel relationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name may be identified by reference to a list,dictionary, or other compendium of terms, permitting ad-hoccategorization by machine-learning algorithms, and/or automatedassociation of data in the data entry with descriptors or into a givenformat. The ability to categorize data entries automatedly may enablethe same training data to be made applicable for two or more distinctmachine-learning algorithms as described in further detail below.Training data used by at least a server 104 may correlate any input dataas described in this disclosure to any output data as described in thisdisclosure.

Continuing to refer to FIG. 1, and as a non-limiting illustrativeexample, at least a server 104 may determine endocrinal age factor byreceiving an endocrinal training set 120 correlating at least a measureof user endocrine function and/or a change in at least a measure of userendocrine function to variances between actuarial life expectancy dataand actual mortality dates. An “actuarial life expectancy datum,” asused herein, is a datum indicating an age of death of a subject person,as predicted by reference to actuarial calculations and/or tables, forinstance as described above; indication may be achieved, withoutlimitation, using a datum containing actuarial life expectancy and/or adatum that may be used to determine actuarial life expectancy. An“actual mortality date” as used herein, is a date on which a personactually died; elements or entries in training data may include dataconcerning people who have died, enabling actual mortality date to bedetermined. An actual mortality date may include a measure of timebetween the date of a given measurement and the death of the person withregard to whom the measurement was taken, which may be represented as acalendar date and/or as a number of years after the date of measurement.A “variance between actuarial life expectancy datum and actual mortalitydate” may include a difference between a life expectancy as determinedby actuarial life expectancy and actual mortality date, which may bedetermined by any suitable measure of difference between numericalquantities, including without limitation by subtraction. “Correlation”in a training data set may include any relation established thereinlinking one datum to another, including inclusion together in a dataelement, row, column, cell, or the like, and/or by giving each a commonindicator and/or label indicative of their correlation in data used tocreate and/or compile training data. Correlation of at least a measureof user endocrine function and/or a change in at least a measure of userendocrine function to variances between actuarial life expectancy dataand actual mortality dates may be accomplished by correlating to acalculated variance, to a factor based on the calculated variance, whichmay include an endocrinal age factor; alternatively or additionally,such correlation may indicate correlation to actuarial life expectancydatum and/or a dataset suitable for looking up actuarial lifeexpectancy, and to actual mortality date, or any other set of data fromwhich such a variance may be deduced. In other words, and by way ofillustration only, an actual mortality date per se may not be in atraining set entry; instead it might be a difference between actuarialand actual life expectancies, a life expectancy plus chronological age(from which variance may be calculated), or the like. Endocrinaltraining set 120 may include a plurality of entries, each entrycorrelating at least a measure of user endocrine function and/or achange in at least a measure of user endocrine function to a variancebetween actuarial life expectancy data and actual mortality date of aperson.

Still referring to FIG. 1, at least a server 104 may perform one or moreprocesses to modify and/or format training data to produce endocrinaltraining set 120. At least a server 104 may, without limitation, modifyentries in training data to contain consistent forms of a variance, forinstance so that a regression process or other supervisedmachine-learning process may operate without converting data toparticular forms during operation; alternatively, supervisedmachine-learning process may perform standardization calculations duringoperation. Other modifications may include receiving a training setcorrelating one or more other biomarkers to variances between actuariallife expectancy data and actual mortality dates, where variances,correlations, and entries may be implemented as described above; forinstance, training data relating endocrinal levels and/or changes inendocrinal levels to variances between actuarial life expectancy dataand actual mortality dates may be unavailable, but a training setrelating one or more additional biomarkers to variances betweenactuarial life expectancy data and actual mortality dates may bereceived. At least a server 104 may use one or more additional machinelearning processes to create endocrinal training set 120 relating atleast a measure of endocrine function to variances between actuariallife expectancy data and actual mortality dates by modifying trainingdata relating one or more additional biomarkers to variances betweenactuarial life expectancy data and actual mortality dates. For instance,and without limitation, at least as server may perform an unsupervisedmachine learning process on training data correlating endocrinalmeasures with additional biomarkers, which may be any biomarkers;unsupervised machine learning may be used to cluster at least a measureof user endocrine function and/or a change in at least a measure of userendocrine function with one or more other biomarkers, for instance toidentify one or more additional biomarkers that are highly correlatedwith endocrinal measures. At least a server 104 may then modify thetraining data to create endocrinal training set 120 by replacing one ormore additional biomarkers in each entry the training data with at leasta measure of endocrine function that is correlated therewith by theunsupervised machine learning set. In an embodiment, this approach maymake it possible to draw upon training data relating one or morebiomarkers to mortality to at least a measure of endocrine function; asdata describing actual dates of death may require data collection over anumber of years, whereas data relating an additional biomarker to atleast a measure of endocrine function may be collected rapidly. In anembodiment, the one or more additional biomarkers may include a firstmeasure of endocrine function, and the at least a measure of endocrinefunction may include a second measure of endocrine function, enablingreplacement of a first measure of endocrine function with a secondmeasure of endocrine function in first endocrine training set. Inanother embodiment, one or more additional biomarkers does not includeany measure of endocrine function; for instance, one or more additionalbiomarkers may include a measure of cardiac function, mobility, body fatpercentage, or the like.

With continued reference to FIG. 1, where endocrinal training set 120correlates at least a measure of user endocrine function to variancesbetween actuarial life expectancy and actual mortality dates, at least aserver 104 may use the endocrinal training set 120 to generate anendocrinal age factor model 124, which may include any machine-learningmodel that receives at least a measure of user endocrine function asinputs and produces an output representing a variance between actuariallife expectancy and a projected actual mortality date, where providingan output “representing” a variance means an output from which avariance can be calculated, including providing the actuarial lifeexpectancy and projected actual mortality date as two output elements,providing the difference between the actuarial life expectancy and aprojected actual mortality date, and/or providing a raw score, forinstance as described above. For example, and without limitation, atleast a server 104 may generate, using a supervised machine-learningprocess, an endocrinal age factor model 124 that receives at least ameasure of user endocrine function as inputs and produces an outputrepresenting a variance between actuarial life expectancy and aprojected actual mortality date. At least a server 104 may thendetermine the user endocrinal age factor using the at least anendocrinal measure and the endocrinal age factor model 124, by inputtingthe at least an endocrinal measure into the endocrinal age factor model124, and receiving an output; output may be a raw score as describedabove, which at least a server 104 may multiply by a weight to obtainthe endocrinal age factor.

With continued reference to FIG. 1, where endocrinal training set 120correlates one or changes in measures of endocrinal function tovariances between actuarial life expectancy and actual mortality dates,at least a server 104 may use the endocrinal training set 120 togenerate an endocrinal age factor model 124, which may include anymachine-learning model that receives one or more changes in endocrinalmeasures as inputs and produces an output representing a variancebetween actuarial life expectancy and a projected actual mortality date,where providing an output “representing” a variance means an output fromwhich a variance can be calculated, including providing the actuariallife expectancy and projected actual mortality date as two outputelements, providing the difference between the actuarial life expectancyand a projected actual mortality date, and/or providing a raw score, forinstance as described above. For example, and without limitation, atleast a server 104 may generate, using a supervised machine-learningprocess, an endocrinal age factor model 124 that receives one or morechanges endocrinal measures as inputs and produces an outputrepresenting a variance between actuarial life expectancy and aprojected actual mortality date. At least a server 104 may thendetermine the user endocrinal age factor using the at least anendocrinal measure and the endocrinal age factor model 124, by inputtinga change in endocrinal measure determined using the at least anendocrinal measure into the endocrinal age factor model 124, andreceiving an output; output may be a raw score as described above, whichat least a server 104 may multiply by a weight to obtain the endocrinalage factor.

Still referring to FIG. 1, at least a server 104 may be configured toclassify user to at least an endocrinal life phase as a function of theat least a measure of endocrinal function. As noted above, over a humanlifespan, endocrinal measures may go up or down in characteristic ways,for instance as described above. At least a server 104 may divide ahuman lifespan into contiguous periods, described herein as “endocrinallife phases,” into which data describing one or more users may beclassified using one or more measures of endocrine function. This may beaccomplished, without limitation, by generating a life phase classifier128 using a supervised machine learning process. For instance, andwithout limitation, at least a server 104 may receive a life phasetraining set 132 including a plurality of entries correlating one ormore measures of endocrine function to chronological ages, and train,using a supervised machine-learning process, a classifier that takes atleast a measure of endocrine function and outputs a classification to anendocrinal life phase, using the life phase training set 132. At least aserver 104 may use life phase classifier 128 to determine an endocrinallife phase of user by providing at least a measure of endocrinefunction, as obtained from user blood sample as described above, as aninput to life phase classifier 128, and receiving an endocrinal lifephase as an input. Any set of training data described in this disclosuremay be limited to a cohort sharing an endocrinal life phase with a user;this may be done by classifying each entry of any set of training datausing life phase classifier 128, and assembling a training setcontaining only entries classified to endocrinal life phases matching anendocrinal life phase of user as described above. In an embodiment, thismay enable use of any machine-learning process as described herein withtraining data representing a cohort of persons sharing an endocrinallife phase with user; more accurate machine-learning results may beproduced using training data so limited.

With continued reference to FIG. 1, at least a server 104 is designedand configured to record a user genetic sample including a measure ofuser telomer length, where “recording” a user genetic sample may includereceiving data describing a user genetic sample that has been extractedfrom a user. A “user genetic sample” as used herein is a sampleincluding any sequence of nucleic acid identified in a user, includingwithout limitation deoxyribonucleic acid (DNA) and/or ribonucleic acid(RNA). DNA may include chromosomal DNA, including without limitationsequences encoding particular genes as well as sequences of DNA disposedbetween or after gene sequences, including without limitation telomeres.Telomeres, as used in this description are caps (repetitive nucleotidesequences) at the end of linear chromosomes of a user. Telomeres aretheorized to play a critical role in facilitating complete chromosomereplication. Telomeres are characterized by noncoding tandem arrays of a“TTAGGG” DNA sequence that are located at the terminal ends of allvertebrate chromosomes, including those of humans. A G-rich singlestranded 3-prime overhang is present at the end of human telomeres; thisoverhang, which may be important for telomere function folds back onitself forming a large loop structure called a telomere loop, or T-loop,that has a shape similar to that of a paper clip. A telomere may bestabilized by a six-protein complex, known as “shelterin,” which mayinclude telomeric repeat binding factor 1 and 2 (TRF1 and TRF2),protection of telomeres 1(POT1), TRF1 and TRF2 interacting nuclearprotein 2 (TIN2), the human ortholog of the yeast repressor/activatorprotein 1 (Rap 1), and TPP1. Telomere lengths have been observed toreduce over a series of cellular mitotic divisions, such that telomerelength and/or changes in telomere length appear to correlate withprocesses of cellular aging and senescence. It is therefore hypothesizedthat telomere length and/or changes thereto may be useful to predictlife expectancy of a person; however, precise predictions have hithertoeluded researchers. A genetic sample may include mRNA, tRNA, or anyother RNA sequence or strand.

Still referring to FIG. 1, extraction of a genetic sample from a usermay include collection of a physically extracted sample from a user,including without limitation a tissue sample, a buccal swab, a fluidsample, a biopsy, or the like; in an embodiment, a blood sample used forextraction of at least a measure of user endocrine function as describedabove may be further processed to extract a genetic sample. Extractionof genetic samples may be performed using any suitable physical process,including separation of nucleic acid from other tissue and/or fluidelements using, without limitation, a centrifuge. Extraction may includeany form of restriction or division of a DNA and/or RNA sequence intosub-sequences, including without limitation using restriction enzymes.Extraction of genetic samples may include one or more variations ofpolymerase chain reaction “PCR” processes, whereby a particular strandof nucleic acid is replicated or “amplified” in a solution of nucleicacid by repeatedly exposing the solution to stimulus, such as heat, thatbreaks base-pair bonds, and then removing the stimulus to allowbase-pair bonds to reform; as a result, a strand or sequence of nucleicacid will bond to free-floating molecules of nucleic acid, forming aninverse copy of itself, which will be separated from the strand orsequence during stimulus, and subsequently each of the strand and theinverse copy will bond to further free-floating molecules. As theabove-described process is repeated, the number of copies of the strandor sequence increases exponentially. Extraction may include any suitableprocess to measure sequence lengths, match sequences, or the like,including without limitation electrophoresis.

Still referring to FIG. 1, methods to identify telomere length mayinclude any suitable method for measuring and/or estimating telomerelength, which may be assessed as any suitable statistical aggregation oftelomere length, including without arithmetic and/or geometric means ofmeasured telomere length. Methods for measurement of telomere length mayinclude, without limitation, quantitative PCR (qPCR) DNA measurementmethods, TRF DNA measurement methods, MMqPCR DNA measurement methods,aTLqPCR DNA measurement methods, STELA DNA measurement methods, Q-FISHMetaphase chromosome measurements, Q-FISH Interphase nuclei (telomere)measurements, PRINS Metaphase chromosomes measurements, PRINS Interphasenuclei (telomere) measurements, Flow-FISH interphase nucleimeasurements, HT Q-FISH interphase nuclei measurements, or any othersuitable technique that may occur to persons skilled in the art uponreviewing the entirety of this disclosure. Measure of telomere lengthmay expressed in units of length; alternatively or additionally,telomere length may be expressed as a proportion or percentage of apreviously measured telomere length. In other words, a measure oftelomere length may include a change in telomere length as measured, forinstance, in a previous iteration of methods as disclosed herein; achange in telomere length may be recorded as an absolute change, arelative change, a percentage, or in any other suitable form that mayoccur to persons skilled in the art upon reviewing the entirety of thisdisclosure.

With continued reference to FIG. 1, at least a server 104 may bedesigned and configured to determine a user telomeric age factor usingthe user telomere length. A “user telomeric age factor,” as used in thisdisclosure, is a factor that may be multiplied by a user's chronologicalage to reflect an effect that telomeric length and/or a change intelomere length has on the user's effective age. Calculation may includeprediction of a variance from actuarial life expectancy for a givenperson, as defined above, as determined based on telomeric length and/orvariation in telomere length. A difference between these two values maybe added to a user chronological age and then divided by the userchronological age to calculate a “raw” factor, for instance as describedabove; this may then be multiplied by a weight to determine thetelomeric age factor, where as above the weight may be calculated tooffset relatedness between telomere length and/or change in telomerelength and other elements used to calculate age factors as describedherein, such as endocrinal age factors. At least a server 104 maydetermine telomeric age factor by retrieving telomeric age factor fromexpert database 116. For instance, and without limitation, one or moreexperts may enter data in expert database 116 indicative of an effect onuser life expectancy; such data may, for instance, be entered asdescribed in further detail below.

Continuing to refer to FIG. 1, and as a non-limiting illustrativeexample, at least a server 104 may determine telomeric age factor byreceiving a telomeric training set 136 correlating telomere lengthand/or change in telomere length to variances between actuarial lifeexpectancy data, defined as above, and actual mortality dates defined asabove. A “variance between actuarial life expectancy datum and actualmortality date” may include any variance between actuarial lifeexpectancy datum and actual mortality date as described above.“Correlation” in telomeric training set 136 may include any relationestablished therein linking one datum to another, including inclusiontogether in a data element, row, column, cell, or the like, and/or bygiving each a common indicator and/or label indicative of theircorrelation in data used to create and/or compile training data.Correlation of telomere length and/or change in telomere length tovariances between actuarial life expectancy data and actual mortalitydates may be accomplished by correlating to a calculated variance, to afactor based on the calculated variance, which may include a usertelomeric age factor or raw factor as described above; alternatively oradditionally, such correlation may indicate correlation to actuariallife expectancy datum and/or a dataset suitable for looking up actuariallife expectancy, and to actual mortality date, or any other set of datafrom which such a variance may be deduced. In other words, and by way ofillustration only, an actual mortality date per se may not be in atraining set entry; instead it might be a difference between actuarialand actual life expectancies, a life expectancy plus chronological age(from which variance may be calculated), or the like. Telomeric trainingset 136 may include a plurality of entries, each entry correlating ameasure of telomere length and/or a change in telomere length to avariance between actuarial life expectancy data and actual mortalitydate of a person.

Still referring to FIG. 1, at least a server 104 may perform one or moreprocesses to modify and/or format training data to produce telomerictraining set 136. At least a server 104 may, without limitation, modifyentries in training data to contain consistent forms of a variance, forinstance so that a regression process or other supervisedmachine-learning process may operate without converting data toparticular forms during operation; alternatively, supervisedmachine-learning process may perform standardization calculations duringoperation. Other modifications may include receiving a training setcorrelating one or more other biomarkers to variances between actuariallife expectancy data and actual mortality dates, where variances,correlations, and entries may be implemented as described above; forinstance, training data relating telomere length and/or changes intelomere length to variances between actuarial life expectancy data andactual mortality dates may be unavailable, but a training set relatingone or more additional biomarkers to variances between actuarial lifeexpectancy data and actual mortality dates may be received. At least aserver 104 may use one or more additional machine learning processes tocreate telomeric training set 136 relating telomere length and/orchanges in telomere length to variances between actuarial lifeexpectancy data and actual mortality dates by modifying training datarelating one or more additional biomarkers to variances betweenactuarial life expectancy data and actual mortality dates. For instance,and without limitation, at least as server may perform an unsupervisedmachine learning process on training data correlating telomere lengthand/or changes in telomere length with additional biomarkers, which maybe any biomarkers; unsupervised machine learning may be used to clustertelomere length and/or changes in telomere length with one or more otherbiomarkers, for instance to identify one or more additional biomarkersthat are highly correlated with telomere length and/or changes intelomere length. At least a server 104 may then modify the training datato create telomeric training set 136 by replacing one or more additionalbiomarkers in each entry the training data with a telomeric lengthand/or a change in telomere length that is correlated therewith by theunsupervised machine learning set. In an embodiment, this approach maymake it possible to draw upon training data relating one or morebiomarkers to mortality to measures of telomere length and/or changes intelomere length, as data describing actual dates of death may requiredata collection over a number of years, whereas data relating anadditional biomarker to at least a measure of telomere length and/orchanges in telomere length may be collected rapidly. At least a server104 may alternatively or additionally limit telomeric training set 136to a cohort of entries that are classified, by life phase classifier128, to an endocrinal file phase to which life phase classifier 128 hasclassified user as described above.

With continued reference to FIG. 1, where telomeric training set 136correlates telomere length to variances between actuarial lifeexpectancy and actual mortality dates, at least a server 104 may use thetelomeric training set 136 to generate a telomeric age factor model 140,which may include any machine-learning model that receives telomerelength as inputs and produces an output representing a variance betweenactuarial life expectancy and a projected actual mortality date, whereproviding an output “representing” a variance means an output from whicha variance can be calculated, including providing the actuarial lifeexpectancy and projected actual mortality date as two output elements,providing the difference between the actuarial life expectancy and aprojected actual mortality date, and/or providing a raw score, forinstance as described above. For example, and without limitation, atleast a server 104 may generate, using a supervised machine-learningprocess, a telomeric age factor model 140 that receives telomere lengthas input and produces an output representing a variance betweenactuarial life expectancy and a projected actual mortality date. Atleast a server 104 may then determine the user telomeric age factorusing measure of telomere length and the telomeric age factor model 140,by inputting the measure of telomere length into the telomeric agefactor model 140, and receiving an output; output may be a raw score asdescribed above, which at least a server 104 may multiply by a weight toobtain the telomeric age factor.

With continued reference to FIG. 1, where telomeric training set 136correlates one or changes in telomere length to variances betweenactuarial life expectancy and actual mortality dates, at least a server104 may use the telomeric training set 136 to generate a telomeric agefactor model 140, which may include any machine-learning model thatreceives a change in telomere length as input and produces an outputrepresenting a variance between actuarial life expectancy and aprojected actual mortality date, where providing an output“representing” a variance means an output from which a variance can becalculated, including providing the actuarial life expectancy andprojected actual mortality date as two output elements, providing thedifference between the actuarial life expectancy and a projected actualmortality date, and/or providing a raw score, for instance as describedabove. For example, and without limitation, at least a server 104 maygenerate, using a supervised machine-learning process, a telomeric agefactor model 140 that receives a change in telomere length input andproduces an output representing a variance between actuarial lifeexpectancy and a projected actual mortality date. At least a server 104may then determine the user telomeric age factor using change in usertelomere length and the telomeric age factor model 140, by inputting achange in telomeric length determined using the measure of user telomerelength into the endocrinal age factor model 124, and receiving anoutput; output may be a raw score as described above, which at least aserver 104 may multiply by a weight to obtain the user telomeric agefactor.

Still referring to FIG. 1, at least a server 104 is configured toprovide a user negative habit factor. A “user negative habit factor,” asused in this disclosure, is a factor that may be multiplied by a user'schronological age to reflect an effect that a habit the user is engagedin has on the user's effective age. A habit a user is engaged in may bea nutritional habit, such as a daily consumption of sugar, fat, fiber,protein, or the like. A habit a user is engaged in may include anexercise habit, which may be measured in terms of a duration per day,week, or the like of cardiovascular exercise, resistance trainingexercise, or other exercise category, a number of steps per week taken,resting and/or total calorie consumption numbers, or the like. A habit auser is engaged in may include a substance abuse habit, including somemeasure of a dosage per period of time consumed of a harmful and/oraddictive substance such as an opiate, alcohol, tobacco, stimulants suchas cocaine, methamphetamine or the like, hallucinogens, narcotics, orother mood-altering chemicals. A habit a user is engaged in may includea sleep habit, including a number of hours per night a user sleeps, anumber of nights a user goes with less than a recommended amount ofsleep, or the like.

Calculation of a user negative habit factor may include prediction of avariance from actuarial life expectancy for a given person, where avariance is a difference between an actuarial life expectancy for thatperson and a projected number of years until death as determined basedon the habit. A difference between these two values may be added to auser chronological age and then divided by the user chronological age tocalculate a “raw” factor; this may then be multiplied by a weight todetermine the user negative habit factor.

In an embodiment, providing a user negative habit factor may includeidentifying a user negative habit. User negative habit may be identifiedby a user entry; for instance, and without limitation, at least a server104 may provide a user with a questionnaire in the form of one or moredata fields requesting that the user identify activities in which theuser engaged. Questions presented to a user may include a number ofservings of alcohol a user consumes during a given period of time suchas a day, a week or a year, a quantity of tobacco, drugs, or othersubstances that a user consumes during a given period of time, a numberof hours a user sleeps in a night, or the like. A user may respond tosuch questions by selecting options corresponding to particular rangesof data, by setting sliders or other indicators of a quantity along acontinuous range, by entering values in drop-down lists, and/or bytyping in numbers or text. Another person may alternatively oradditionally enter information concerning a user's negative habits.

Alternatively or additionally, and still referring to FIG. 2,identifying a user negative habit may include receiving a training setcorrelating blood tests to negative habits, for instance by matchingblood test results of individual who self-report particular negativehabits. Identifying a user negative habit may include generating a usernegative habit identifier model using a supervised machine-learningalgorithm and the training set; negative habit identifier model may begenerated, without limitation, using a classification algorithm, so thatnegative habit identifier may match a user blood test pattern to a mostlikely habit or a set of most likely habits of which user may partake.Identifying a user negative habit may include producing a negative habitoutput from the negative habit identifier model using the user bloodtest and identifying the user negative habit as a function of thenegative habit output.

At least a system may provide the user negative habit factor based onthe user negative habit. In an embodiment, this may be performed byconsulting expert database 116; that is, one or more experts may providedata indicating a likely impact on life expectancy of a particularnegative habit. For instance, an expert may provide an entry indicatingthat a lifelong smoking habit of one pack of cigarettes per day removesan average of 10 years of life expectancy; another expert may providedata indicative of a number of years of life expectancy lost toalcoholism, drug habits, consumption of too much saturated fat, or thelike. Expert submissions may be based on information including withoutlimitation studies or other academic materials.

Continuing to refer to FIG. 1, and as a non-limiting illustrativeexample, at least a server 104 may determine negative habit factor byreceiving a negative habit training set 144 correlating one or more usernegative habits and/or one or more changes in user negative habits tovariances between actuarial life expectancy data and actual mortalitydates. “Actuarial life expectancy data” and “actual mortality date” maybe defined as described above. A “variance between actuarial lifeexpectancy datum and actual mortality date” may include any suchvariance as described above. “Correlation” in a training data set mayinclude any relation established therein linking one datum to another,including inclusion together in a data element, row, column, cell, orthe like, and/or by giving each a common indicator and/or labelindicative of their correlation in data used to create and/or compiletraining data. Correlation of one or more user negative habits and/orone or more changes in user negative habits to variances betweenactuarial life expectancy data and actual mortality dates may beaccomplished by correlating to a calculated variance, to a factor basedon the calculated variance, which may include a user negative habitfactor; alternatively or additionally, such correlation may indicatecorrelation to actuarial life expectancy datum and/or a dataset suitablefor looking up actuarial life expectancy, and to actual mortality date,or any other set of data from which such a variance may be deduced. Inother words, and by way of illustration only, an actual mortality dateper se may not be in a training set entry; instead it might be adifference between actuarial and actual life expectancies, a lifeexpectancy plus chronological age (from which variance may becalculated), or the like. Negative habit training set 144 may include aplurality of entries, each entry correlating one or more user negativehabits and/or one or more changes in user negative habits to a variancebetween actuarial life expectancy data and actual mortality date of aperson.

Still referring to FIG. 1, at least a server 104 may perform one or moreprocesses to modify and/or format training data to produce negativehabit training set 144. At least a server 104 may, without limitation,modify entries in training data to contain consistent forms of avariance, for instance so that a regression process or other supervisedmachine-learning process may operate without converting data toparticular forms during operation; alternatively, supervisedmachine-learning process may perform standardization calculations duringoperation. Other modifications may include receiving a training setcorrelating one or more other biomarkers to variances between actuariallife expectancy data and actual mortality dates, where variances,correlations, and entries may be implemented as described above; forinstance, training data relating one or more user negative habits and/orchanges in user negative habits to variances between actuarial lifeexpectancy data and actual mortality dates may be unavailable, but atraining set relating one or more additional biomarkers to variancesbetween actuarial life expectancy data and actual mortality dates may bereceived. At least a server 104 may use one or more additional machinelearning processes to create negative habit training set 144 relatingone or more user negative habits and/or changes in user negative habitsto variances between actuarial life expectancy data and actual mortalitydates by modifying training data relating one or more additionalbiomarkers to variances between actuarial life expectancy data andactual mortality dates. For instance, and without limitation, at leastas server may perform an unsupervised machine learning process ontraining data correlating one or more user negative habits and/orchanges in user negative habits with additional biomarkers, which may beany biomarkers; unsupervised machine learning may be used to cluster oneor more user negative habits and/or changes in user negative habits withone or more other biomarkers, for instance to identify one or moreadditional biomarkers that are highly correlated with one or more usernegative habits and/or changes in user negative habits. At least aserver 104 may then modify the training data to create negative habittraining set 144 by replacing one or more additional biomarkers in eachentry of the training data with a one or more user negative habitsand/or changes in user negative habits that is correlated therewith bythe unsupervised machine learning set. In an embodiment, this approachmay make it possible to draw upon training data relating one or morebiomarkers to mortality to measures of one or more user negative habitsand/or changes in user negative habits, as data describing actual datesof death may require data collection over a number of years, whereasdata relating an additional biomarker to one or more user negativehabits and/or changes in user negative habits may be collected rapidly.Any negative habit training set 144 may be limited to a cohort ofpersons and/or data sets, such as limiting to entries corresponding topersons classified to an endocrinal life phase to which user has beenclassified as described above.

With continued reference to FIG. 1, where negative habit training set144 correlates user negative habits to variances between actuarial lifeexpectancy and actual mortality dates, at least a server 104 may use thenegative training set to generate an negative habit factor model 148,which may include any machine-learning model that receives one or moreuser negative habits as inputs and produces an output representing avariance between actuarial life expectancy and a projected actualmortality date, where providing an output “representing” a variancemeans an output from which a variance can be calculated, includingproviding the actuarial life expectancy and projected actual mortalitydate as two output elements, providing the difference between theactuarial life expectancy and a projected actual mortality date, and/orproviding a raw score, for instance as described above. For example, andwithout limitation, at least a server 104 may generate, using asupervised machine-learning process, a negative habit factor model 148that receives one or more user negative habits as inputs and produces anoutput representing a variance between actuarial life expectancy and aprojected actual mortality date. At least a server 104 may thendetermine the user negative habit factor using an identified at least auser negative habit and the negative habit factor model 148, byinputting the one or more user negative habits into the negative habitfactor model 148, and receiving an output; output may be a raw score asdescribed above, which at least a server 104 may multiply by a weight toobtain the user negative habit factor.

With continued reference to FIG. 1, where negative habit training set144 correlates one or changes in user negative habits to variancesbetween actuarial life expectancy and actual mortality dates, at least aserver 104 may use the negative habit training set 144 to generate anegative habit factor model 148, which may include any machine-learningmodel that receives a change in one or more user negative habits inputand produces an output representing a variance between actuarial lifeexpectancy and a projected actual mortality date, where providing anoutput “representing” a variance means an output from which a variancecan be calculated, including providing the actuarial life expectancy andprojected actual mortality date as two output elements, providing thedifference between the actuarial life expectancy and a projected actualmortality date, and/or providing a raw score, for instance as describedabove. For example, and without limitation, at least a server 104 maygenerate, using a supervised machine-learning process, a negative habitfactor model 148 that receives a change in one or more user negativehabits and produces an output representing a variance between actuariallife expectancy and a projected actual mortality date. At least a server104 may then determine the user negative habit factor using change inone or more user negative habits and the telomeric age factor model 140,by inputting a change in user negative habit determined negative habitfactor model 148, and receiving an output; output may be a raw score asdescribed above, which at least a server 104 may multiply by a weight toobtain the user negative habit factor.

In an embodiment, and still referring to FIG. 1, at least a server 104may be provided with one or more weights to multiply with raw factors toobtain user endocrinal age factor, user telomeric age factor and/or usernegative habit factor. Such weights may be provided by experts; forinstance, at least a server 104 may recover weights from expert database116. Alternatively or additionally at least a server 104 may generateweights using unsupervised machine-learning procedures. For instance,and without limitation, at least a server 104 may receive a factordependency training set 152 having entries correlating at least ameasure of endocrine function, telomere length, and negative habits. Anunsupervised machine learning process may use factor dependency trainingset 152 to determine a set of outputs indicating a degree ofinterrelatedness between at least a measure of endocrine function,telomere length, and negative habits; this may be used to create afactor dependence model, which may receive, for instance, an input setrepresenting at least a measure of endocrine function, telomere length,and negative habit data for a user and outputting weights to bemultiplied by each raw factor so that each of user telomeric age factor,user endocrinal age factor and user negative habit factor is reduced inproportion to its likely dependency on the other factors and/or theirdependency on it. This may prevent at least a server 104 fromoverestimating the combined impact of all three factors on lifeexpectancy, by accounting for the degree to which all three factors maydepend upon each other.

Referring now to FIG. 2, an exemplary embodiment of a user database 112is illustrated. One or more database tables in user database 112 mayinclude, as a non-limiting example, a blood test table 200, which mayrecord data received by at least a server 104 regarding a user bloodtest as described above. One or more database tables in user database112 may include, as a non-limiting example, an endocrine measure table204, which may record data received by at least a server 104 regardingat least a measure of user endocrine function as described above. One ormore database tables in user database 112 may include, as a non-limitingexample, a telomere measure table 208, which may record data received byat least a server 104 regarding at least a measure of telomere length asdescribed above. One or more database tables in user database 112 mayinclude, as a non-limiting example, negative habit table 212, which mayrecord data received by at least a server 104 regarding a user negativehabit as described above.

Referring now to FIG. 3, an exemplary embodiment of an expert database116 is illustrated. Expert database 116 may, as a non-limiting example,organize data stored in the expert database 116 according to one or moredatabase tables. One or more database tables may be linked to oneanother by, for instance, common column values. For instance, a commoncolumn between two tables of expert database 116 may include anidentifier of an expert submission, such as a form entry, textualsubmission, expert paper, or the like, for instance as defined below; asa result, a query may be able to retrieve all rows from any tablepertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

Still referring to FIG. 3, one or more database tables in expertdatabase 116 may include, as a non-limiting example, an expert endocrinetable 300. Expert endocrine table 300 may include any informationprovided by one or more experts regarding at least a measure of userendocrine function, user endocrinal age factor, and/or other expert dataregarding endocrinal information as described in this disclosure. One ormore database tables in expert database 116 may include, as anon-limiting example, an expert telomere table 304. Expert telomeretable 304 may include any information provided by one or more expertsregarding user telomer length, user telomeric age factor, and/or otherexpert data regarding telomere related information as described in thisdisclosure. One or more database tables in expert database 116 mayinclude, as a non-limiting example, an expert negative habit table 304.Expert negative habit table 304 may include any information provided byone or more experts regarding user negative habits length, user negativehabit factor, and/or other expert data regarding negative habit relatedinformation as described in this disclosure. One or more database tablesin expert database 116 may include, as a non-limiting example, an expertintervention table 308. Expert intervention table 308 may include anyinformation provided by one or more experts regarding interventions,intervention elements, and/or vectors associated therewith, as describedin further detail below.

In an embodiment, and still referring to FIG. 3, a forms processingmodule 312 may sort data entered in a submission via a graphical userinterface 316 receiving expert submissions by, for instance, sortingdata from entries in the graphical user interface 316 to relatedcategories of data; for instance, data entered in an entry relating inthe graphical user interface 316 to endocrinal data may be sorted intovariables and/or data structures for endocrinal data, which may beprovided to expert endocrinal table 300, while data entered in an entryrelating to telomere length may be sorted into variables and/or datastructures for the storage of, telomere length data, such as experttelomeric table 304. Where data is chosen by an expert from pre-selectedentries such as drop-down lists, data may be stored directly; where datais entered in textual form, a language processing module may be used tomap data to an appropriate existing label, for instance using a vectorsimilarity test or other synonym-sensitive language processing test tomap data to existing labels and/or categories. Similarly, data from anexpert textual submissions 320, such as accomplished by filling out apaper or PDF form and/or submitting narrative information, may likewisebe processed using language processing module.

Still referring to FIG. 3, a language processing module 324 may includeany hardware and/or software module. Language processing module 324 maybe configured to extract, from the one or more documents, one or morewords. One or more words may include, without limitation, strings of oneor characters, including without limitation any sequence or sequences ofletters, numbers, punctuation, diacritic marks, engineering symbols,geometric dimensioning and tolerancing (GD&T) symbols, chemical symbolsand formulas, spaces, whitespace, and other symbols, including anysymbols usable as textual data as described above. Textual data may beparsed into tokens, which may include a simple word (sequence of lettersseparated by whitespace) or more generally a sequence of characters asdescribed previously. The term “token,” as used herein, refers to anysmaller, individual groupings of text from a larger source of text;tokens may be broken up by word, pair of words, sentence, or otherdelimitation. These tokens may in turn be parsed in various ways.Textual data may be parsed into words or sequences of words, which maybe considered words as well. Textual data may be parsed into “n-grams”,where all sequences of n consecutive characters are considered. Any orall possible sequences of tokens or words may be stored as “chains”, forexample for use as a Markov chain or Hidden Markov Model.

Still referring to FIG. 1, language processing module 324 may compareextracted words to categories of data to be analyzed; such data forcomparison may be entered on at least a server 104 as described aboveusing expert data inputs or the like. In an embodiment, one or morecategories may be enumerated, to find total count of mentions in suchdocuments. Alternatively or additionally, language processing module 324may operate to produce a language processing model. Language processingmodel may include a program automatically generated by at least a serverand/or language processing module 324 to produce associations betweenone or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations between such words and otherelements of data analyzed, processed and/or stored by system 100.Associations between language elements, may include, without limitation,mathematical associations, including without limitation statisticalcorrelations between any language element and any other language elementand/or language elements. Statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating, for instance, a likelihood that a given extracted wordindicates a given category of physiological data, a given relationshipof such categories to prognostic labels, and/or a given category ofprognostic labels. As a further example, statistical correlations and/ormathematical associations may include probabilistic formulas orrelationships indicating a positive and/or negative association betweenat least an extracted word and/or a given category of data; positive ornegative indication may include an indication that a given document isor is not indicating a category of data.

Still referring to FIG. 3, language processing module 324 and/or atleast a server 104 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMI inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 324may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 3, language processing module 324 may use acorpus of documents to generate associations between language elementsin a language processing module 324, and at least a server 104 may thenuse such associations to analyze words extracted from one or moredocuments. Documents may be entered into classification device 104 bybeing uploaded by an expert or other persons using, without limitation,file transfer protocol (FTP) or other suitable methods for transmissionand/or upload of documents; alternatively or additionally, where adocument is identified by a citation, a uniform resource identifier(URI), uniform resource locator (URL) or other datum permittingunambiguous identification of the document, classification device 104may automatically obtain the document using such an identifier, forinstance by submitting a request to a database or compendium ofdocuments such as JSTOR as provided by Ithaka Harbors, Inc. of New York.

Data may be extracted from expert papers 328, which may include withoutlimitation publications in medical and/or scientific journals, bylanguage processing module 324 via any suitable process as describedherein. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional methods whereby novelterms may be separated from already-classified terms and/or synonymstherefore, as consistent with this disclosure.

Referring again to FIG. 1, at least a server 104 may be configured tocalculate at least a user effective age. Calculating at least a usereffective age includes multiplying a user chronological age by the usertelomeric age factor, the user endocrinal age factor, and the usernegative habit factor;

Still referring to FIG. 1, derive a user health quality vector 156; atuser health quality vector 156 may be a data structure that represents aquantitative measure of a degree of importance a user places on each ofa plurality of user goals, including decrease in user effective age, orequivalently increase in life expectancy, and at least one otherdistinct priority. A user health quality vector 156, as defined in thisdisclosure, is n n-tuple of values, where n is at least two values, asdescribed in further detail below. Each value of n-tuple of values mayrepresent a measurement or other quantitative value associated with agiven category of data, or attribute, examples of which are provided infurther detail below; a vector may be represented, without limitation,in n-dimensional space using an axis per category of value representedin n-tuple of values, such that a vector has a geometric directioncharacterizing the relative quantities of attributes in the n-tuple ascompared to each other. Two vectors may be considered equivalent wheretheir directions, and/or the relative quantities of values within eachvector as compared to each other, are the same; thus, as a non-limitingexample, a vector represented as [5, 10, 15] may be treated asequivalent, for purposes of this disclosure, as a vector represented as[1, 2, 3]. Vectors may be more similar where their directions are moresimilar, and more different where their directions are more divergent;however, vector similarity may alternatively or additionally bedetermined using averages of similarities between like attributes, orany other measure of similarity suitable for any n-tuple of values, oraggregation of numerical similarity measures for the purposes of lossfunctions as described in further detail below. Any vectors as describedherein may be scaled, such that each vector represents each attributealong an equivalent scale of values. Each vector may be “normalized,” ordivided by a “length” attribute, such as a length attribute l as derivedusing a Pythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)},where a_(i) is attribute number i of the vector. Scaling and/ornormalization may function to make vector comparison independent ofabsolute quantities of attributes, while preserving any dependency onsimilarity of attributes; this may, for instance be advantageous whereeach vector represents a weighing of user priorities, and/or is to becompared to such a weighing of user priorities.

Continuing to refer to FIG. 1, user health quality vector 156 includes aplurality of health vector entries, which are attributes of user healthquality vector 156 as described above. Plurality of health vectorentries includes an effective age reduction value indicating a degree ofimportance of effective age reduction. Plurality of health vectorentries includes at least a life quality objective value indicating anumerical measure of a user life quality priority. At least a lifequality objective may include, for instance, an attribute indicating adegree of importance to user of cost of an action that may be taken toimprove life expectancy, such as an intervention and/or interventionelement as described in further detail below. At least a life qualityobjective may include an attribute indicating a degree of importance touser of a negative habit. At least a life quality objective may includean attribute indicating a degree of importance to user of schedulecommitment, where, for instance, a larger number may indicate a greaterreluctance to schedule regular sessions, exercise programs, bedtimes, orthe like. At least a life quality objective may include an attributeindicating a degree of importance to user of time commitment, which mayinclude a numerical measure of a degree to which user is bothered byhaving to set aside a given amount of time in a week, day, month and/oryear. At least a life quality objective may include an attributeindicating a degree of importance to user of a change in diet. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various additional attributes that may be used for at leasta life quality objective in user health quality vector 156.

Referring again to FIG. 1, at least a server 104 may derive user qualityhealth vector by receiving at least a user input. For instance, agraphical user interface may display at user device options to rate oneor more priorities absolutely and/or relatively to each other, forinstance by providing a numerical rating scale with radio buttons and/ordrop-down lists, sliders where a user may set relative importance alonga continuum for each user health quality vector 156 attribute, and/ortextual entry fields wherein a user may enter numbers reflecting user'spersonal degree of importance for each field.

Alternatively or additionally, and still referring to FIG. 1, derivingthe user health quality vector 156 may include generating a defaultvector; a default vector may contain default values that represent a“first guess” by system 100 for what user's relative priorities arelikely to be. Default vector may be stored in and/or retrieved fromexpert database 116, which may be populated based on an expertdetermination of likely priorities. Alternatively, a person acquaintedwith user may enter, in a display as described above, what that personbelieves user's priorities are likely to be; multiple such entries maybe aggregated, averaged, or the like. In an embodiment, at least aserver 104 may use a machine-learning process to generate a defaultvector; this may be performed by predicting a user's likely prioritiesand/or preferences based on previously determined priorities and/orpreferences of another person. For instance, generating a default vectormay include receiving a default vector training set correlating a cohortof individual information to individual health quality vectors. Defaultvector training set may include a plurality of entries, each entrycorresponding to a different person; entries may be anonymized topreserve individual privacy. Each entry of plurality of entries mayinclude a set of personal data, pertaining to a person represented bythe entry, which may include any information suitable for inclusion inuser database 112 as described above, including user preferences,habits, health information including without limitation blood test,endocrinal, genetic, and/or telomeric data, user negative habit data,user demographic data, and the like. Each entry may also include a userhealth quality vector 156, which may include any element and/or elementssuitable for inclusion in user health quality vector 156 as describedabove. In an embodiment, at least a server 104 may classify each entryto an endocrinal life phase, for instance and without limitation usinglife phase classifier 128. Endocrinal life phase may be added to eachentry and used as a further matching criterion; alternatively oradditionally, default vector training set may be limited to a cohort ofentries having endocrinal life phases matching an endocrinal life phaseof user, which may be determined as described above.

Still referring to FIG. 1, at least a server 104 may generating a set ofuser data regarding the user; set of user data may be generated to matchcategories of data in entries in default vector training set. In anembodiment set of user data may be generated by querying user database112. Alternatively or additionally, one or more elements of set of userdata may be obtained by prompting user to enter the one or more elementsat a user device and receiving the one or more elements in response tothe prompting; one or more elements may be obtained, alternatively oradditionally, by prompting another person, for instance at or via anadditional client device, to provide the one or more elements of data,and receiving the one or more elements in response. The above-describedmethods may be combined; for instance, at least a server 104 may queryuser database 112 to obtain some elements of user data, determine thatone or more elements matching categories in default vector database aremissing, and prompt user and/or another person to provide such elements.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which user data may becollected and/or generated consistently with this disclosure.

With continued reference to FIG. 1, at least a server 104 may derivedefault vector from training set as a function of set of user data,using any suitable machine learning algorithm. As a non-limitingexample, at least a server 104 may derive default vector from trainingset using a lazy-learning process, which may be a K-nearest neighborsalgorithm; K-nearest neighbors may return a single matching entry, or aplurality of matching entries. Where a plurality of matching entries arereturned, at least a server 104 may derive default vector from pluralityof matching entries by aggregating user health quality vector 156 s ofmatching entries; aggregation may be performed using any suitable methodfor aggregation, including component-wise addition followed bynormalization, component-wise calculation of arithmetic means, or thelike. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which multiple user healthquality vector 156 s may be combined to create a default vector.

Still referring to FIG. 1, deriving the user health quality vector 156may additionally include displaying a default vector to the user.Default vector may be displayed to user via a user device. In anembodiment, display of default vector to user may be performed bypopulating data entry fields usable for user to enter values of userhealth vector with values taken from default vector. Such populated dataentry fields may be displayed to user, indicating a first guess atuser's likely preferences. At least a server 104 may receive a usercommand modifying the default vector; command may be received in theform of a modification and/or replacement by user of a value displayedin a user entry field. At least a server 104 may derive user healthquality vector 156 using the default vector and the user command; forinstance, and without limitation, system may adopt user modifications todefault vector to produce a user health quality vector 156.

Still referring to FIG. 1, user health quality vector 156 may be storedin memory of at least a server 104, including without limitation in userdatabase 112 as described above. User health quality vector 156 may beupdated periodically; for instance a user may modify user health qualityvector 156 via a user interface, for instance to change one or morerelative priorities to match user health quality vector 156. User mayenter a command to view user health quality vector 156, modify one ormore parameters and/or attributes of user health quality vector 156, andcause at least a server 104 to store modified at least a user healthquality vector 156.

With continued reference to FIG. 1, at least a server 104 is designedand configured to generate a plurality of interventions. An“intervention” is a set of actions, called “intervention elements,” auser can take that, taken together, probably will reduce effective age,where probability is determined by a process as described herein forpredicting that an intervention will reduce effective age. Each suchintervention element may be stored in an intervention element database160, which may be implemented in any manner suitable for implementationof user database 112 as described above.

Referring now to FIG. 4, an exemplary embodiment of intervention elementdatabase 160 is illustrated. Intervention element database 160 mayinclude a dietary change table 400, which may contain, withoutlimitation, intervention elements corresponding to dietary changes, suchas without limitation a reduction in the daily consumption of aparticular nutrient, an increase in the daily consumption of aparticular nutrient, a decrease in the daily consumption of a givencategory of food, an increase in the daily consumption of a givencategory of food, or the like. For instance, and without limitation, anintervention element may include cessation of meat consumption, anaddition of one serving of fruit per day, a halving of daily saturatedfat intake as measured in calories and/or grams of saturated fat, or thelike. Intervention elements pertaining to nutritional goals may listparticular meals, meal plans, food elements, or the like, together withcorresponding nutritional goals met by such meals, meal plans, and/orfood elements. Intervention element database 160 may include an exerciseelement table 404, which may contain intervention elements that includeone or more measurable exercise goals, such as a goal to take sometarget number of steps per day, a goal to burn a target number ofcalories per day, a goal to engage in a certain amount of cardiovascularexercise at a given intensity level, as represented for instance by anumber on a discrete scale from 1 to 10, where 1 is a minimal intensityand 10 is a maximal intensity, a goal to engage in a certain amount ofresistance training at a given intensity level, which may be similarlyrepresented, a goal to spend a certain quantity of time per daystretching, or the like. Intervention elements pertaining to exercisegoals may include particular forms of exercise, such as jogging, biking,weightlifting, or the like, which may list corresponding exercise goalsthat match the intervention elements. Intervention element database 160may include, without limitation, a sleep table 408, which may record oneor more intervention elements to sleep goals, such as a goal to sleep acertain number of hours per week or per day, to set a fixed bedtime, orthe like. Intervention element table may include, without limitation, anegative habit table 412, which may record information elements relatingto a cessation or reduction of a negative habit, such as tobaccoconsumption, alcohol consumption, gambling, drug use, or the like; suchintervention elements may alternatively or additionally list particularprograms and/or protocols for reduction in bad habits, such as 12-stepprograms or the like.

Referring again to FIG. 1, at least a server 104 may generate eachintervention of plurality of interventions by combining interventionelements, which may be retrieved from intervention element database 160.Intervention elements for combinations may be selected according tointervention elements likely to improve a particular user's state ofhealth; such elements may be identified, without limitation, usingexpert inputs; for instance expert inputs may link particular endocrinallevels and/or change in endocrinal levels to particular nutritionalgoals, exercise goals, sleep goals, or cessation of bad habits, whichmay in turn be used to retrieve particular intervention elements fromintervention element database 160. As another non-limiting example, oneor more expert inputs may identify reductions in bad habits that mayimprove user life expectancy, one or more programs that may aid incessation of one or more bad habits, or the like. One or more expertinputs may propose one or more combinations of intervention elementsthat an expert may opine are especially useful, and/or that an expertmay have viewed in the past as efficacious or convenient.

Still referring to FIG. 1, intervention element combinations may beselected using machine learning processes. For instance, an interventiontraining set including a plurality of entries may be received, eachentry including a profile of a person having given endocrinal levels,telomere lengths, and/or negative habits, an intervention engaged in bythat person, and effect of the intervention on a life expectancy of thatperson; intervention training set may be limited to cohorts of personssharing an endocrinal life phase with user, persons sharing achronological age with user, and/or persons sharing an effective agewith user. A machine-learning process may identify persons having thegreatest similarity to user, where similarity is matched according toendocrinal levels, telomere lengths, and/or negative habits, combinedwith a life-expectancy improvement goal from user health quality vector156. A list of interventions may be selected using a K-nearest neighborsalgorithm, a classifier, a lazy-learning algorithm, or a “best match”algorithm. Alternatively or additionally, an intervention orintervention component may be generated and/or selected by submittingany blood test data, genetic data, measure of endocrine function,telomere length or the like to a diagnostic engine configured togenerate “ameliorative process labels,” defined as labels describing oneor more forms of actions that may tend to improve conditions and/orpotential future conditions of a person, receiving, from the diagnosticengine, at least an ameliorative process label, and generating and/orselecting an intervention and/or intervention component as a function ofthe at least an ameliorative process label; such a diagnostic engine maybe implemented, without limitation, as disclosed in U.S. Nonprovisionalpatent application Ser. No. 16/354,119, filed on Mar. 14, 2019, andentitled ARTIFICIAL INTELLIGENCE SYSTEMS AND METHODS FOR VIBRANTCONSTITUTIONAL GUIDANCE, the entirety of which is incorporated byreference herein.

With continued reference to FIG. 1, each intervention of the pluralityof interventions includes an intervention vector including a pluralityof intervention vector entries; intervention vector, in any givenembodiment, may have entries corresponding to entries in user healthquality vector 156. For instance, and without limitation, eachintervention element may have an associated vector, which may be chosenas a subset of attributes of intervention element listed in a database,where each attribute indicates an effect each intervention element hason an attribute in user health quality vector 156; for example, aparticular intervention element may have a large impact on lifeexpectancy, represented by a large number in an attribute fieldassociated with change to life expectancy, a low cost, represented by asmall number in a field associated with cost, and require a large weeklytime commitment, indicated by a large number in a field associated withweekly time commitment. Vectors associated with intervention elementsmay be stored in intervention element database 160 and linked toparticular intervention elements; these vectors may, without limitation,be populated by expert inputs, aggregated user ratings, or the like. Atleast a server 104 may generate a vector of each intervention ofplurality of interventions by combining intervention elements, forinstance via component-wise addition; each intervention's vector maythen be scaled and/or normalized as described above. As a result, eachintervention vector entry may indicate a degree of relative impact on afactor represented by a health vector entry resulting from theintervention.

Still referring to FIG. 1, at least a server 104 is designed andconfigured to select an intervention from the plurality ofinterventions. In an embodiment, selecting the intervention furtherincludes generating a loss function of the plurality of interventionsand the user health quality vector 156, minimizing the loss function,and selecting an intervention from the plurality of interventions as afunction of minimizing the loss function. A “loss function”, as usedherein is an expression of an output of which an optimization algorithmminimizes to generate an optimal result. As a non-limiting example, atleast a server 104 may select an intervention having an associatedvector that minimizes a measure of difference from user health qualityvector 156; measure of difference may include, without limitation, ameasure of geometric divergence between intervention vector and userhealth quality vector 156, such as without limitation cosine similarity,or may include any suitable error function measuring any degree ofdivergence and/or aggregation of degrees of divergence, betweenattributes of user heath quality vector and intervention vectors.Selection of different loss functions may result in identification ofdifferent interventions as generating minimal outputs. Alternatively oradditionally, each of user health quality vector 156 and eachintervention vector may be represented by a mathematical expressionhaving the same form as mathematical expression; at least a server 104may compare the former to the latter using an error functionrepresenting average difference between the two mathematicalexpressions. Error function may, as a non-limiting example, becalculated using the average difference between coefficientscorresponding to each variable. An intervention having a mathematicalexpression minimizing the error function may be selected, asrepresenting an optimal expression of relative importance of variablesto a system or user. In an embodiment, error function and loss functioncalculations may be combined; for instance, a variable resulting in aminimal aggregate expression of error function and loss function, suchas a simple addition, arithmetic mean, or the like of the error functionwith the loss function, may be selected, corresponding to an option thatminimizes total variance from optimal variables while simultaneouslyminimizing a degree of variance from a set of priorities correspondingto variables. Coefficients of mathematical expression and/or lossfunction may be scaled and/or normalized; this may permit comparisonand/or error function calculation to be performed without skewing byvaried absolute quantities of numbers. Server may select a plurality ofinterventions to user; for instance, ranking may be maintained ofinterventions according to a degree to which they minimize lossfunction, and a number of highest-ranking interventions, such as the tenhighest ranking interventions or the like, may be selected.

In an embodiment, and still referring to FIG. 1, at least a server 104may be configured to present selected intervention and/or interventionsto user; for instance, intervention vector each selected interventionmay be presented. This may, in an embodiment, have the result that theuser is able to see an impact on life expectancy of a givenintervention, as well as its impact on one or more additional prioritiesthat user has specified in user health quality vector 156. Server mayadditionally select an intervention maximizing impact on lifeexpectancy, for instance by running loss function against a vectorhaving all elements except life expectancy impact set to zero; this maybe displayed as well to inform the user of a maximal possible impact onlife expectancy. In an embodiment, at least a server 104 may receiveuser input modifying user health quality vector 156, for instance asdescribed above; at least a server 104 may repeat above-describedprocesses for selection of one or more interventions, including anycost-function process, and display selected interventions a second time.

Turning to FIG. 5, an exemplary embodiment of a method 500 of selectingan intervention based on effective age is illustrated. At step 505, atleast a server 104 records a user blood test indicating at least ameasure of user endocrine function; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-4. At step 510,at least a server 104 determines a user endocrinal age factor using theat least a measure of user endocrine function; this may be implemented,without limitation, as described above in reference to FIGS. 1-4. Forinstance, and without limitation, determining user endocrinal age factormay include receiving training data correlating at least a measure ofuser endocrine function to variances between actuarial life expectancydatum and actual mortality dates, generating, using a supervisedmachine-learning process, an endocrinal age factor model 124 thatreceives at least a measure of user endocrine function as inputs andproduces an output representing a variance between actuarial lifeexpectancy and a projected actual mortality date, and determining theuser endocrinal age factor using the at least a measure of endocrinefunction and the endocrinal age factor model 124. Alternatively oradditionally, determining the user endocrinal age factor may includereceiving training data correlating one or more changes in endocrinalmeasures to variances between actuarial life expectancy datum and actualmortality dates, generating, using a supervised machine-learningprocess, an endocrinal age factor model 124 that receives one or morechanges in endocrinal measures as inputs and produces an outputrepresenting a variance between actuarial life expectancy and aprojected actual mortality date, calculating at least a change in anendocrinal measure using the at least a measure of endocrine function,and determining the user endocrinal age factor using the at least achange in the endocrine measure and the endocrinal age factor model 124.

At step 515, and still referring to FIG. 5, at least a server 104records a user genetic sample, where the user genetic sample includes ameasure of user telomere length; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-4. At step 520,at least a server 104 determines a user telomeric age factor using theuser telomere length; this may be implemented, without limitation, asdescribed above in reference to FIGS. 1-4. For instance, and withoutlimitation, determining the user telomeric age factor may includereceiving training data correlating telomere length to variances betweenactuarial life expectancy datum and actual mortality dates, generating,using a supervised machine-learning process, a telomeric age factormodel 140 that receives telomeric length as input and produces an outputrepresenting a variance between actuarial life expectancy and aprojected actual mortality date, and determining the user telomeric agefactor using the user telomeric length and the telomeric age factormodel 140. In an embodiment, at least a server 104 may identify, using afirst unsupervised machine learning process, a correlation between atleast a biomarker and telomere length, receive training data correlatingthe at last a biomarker to variances between actuarial life expectancydatum and actual mortality dates, and generate the training datacorrelating telomere length to variances between actuarial lifeexpectancy datum and actual mortality dates using the training datacorrelating the at last a biomarker to variances between actuarial lifeexpectancy datum and actual mortality dates and the correlation ofbetween the at least a biomarker and telomere length.

With continued reference to FIG. 5, at step 525 at least a server 104determine a user negative habit factor; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-4. As anon-limiting example, determining a user negative habit factor mayinclude identifying a user negative habit and providing the usernegative habit factor based on the user negative habit. Identifying usernegative habit may include receiving a training set correlating bloodtests to negative habits, generating negative habit identifier modelusing a supervised machine-learning algorithm and the training set,producing a negative habit output from the negative habit identifiermodel using the user blood test, and identifying the user negative habitas a function of the negative habit output. Determining the usernegative habit factor may include receiving training data correlatinguser negative habit to variances between actuarial life expectancy datumand actual mortality dates, generating, using a supervisedmachine-learning process, a user negative habit factor model 148 thatreceives negative habit ID as input and produces an output representinga variance between actuarial life expectancy and a projected actualmortality date, and determining the user negative habit age factor usingthe user telomeric length and the telomeric age factor model 140.

At step 530, and still referring to FIG. 5, at least a server 104calculates at least a user effective age, wherein calculating the atleast a user effective age further comprises multiplying a userchronological age by the user telomeric age factor, the user endocrinalage factor, and the user negative habit factor; this may be implemented,without limitation, as described above in reference to FIGS. 1-4.

At step 535, and continuing to refer to FIG. 5, at least a server 104derives a user health quality vector 156, the user health quality vector156 including a plurality of health vector entries including aneffective age reduction value indicating a degree of importance ofeffective age reduction and at least a life quality objective valueindicating a numerical measure of a user life quality priority; this maybe implemented, without limitation, as described above in reference toFIGS. 1-4. For instance, and without limitation, deriving the userhealth quality vector 156 may include generating a default vector,displaying the default vector to the user, receiving a user commandmodifying the default vector, and deriving the user health qualityvector 156 using the default vector and the user command. Generatingdefault vector may include receiving a training set correlating a cohortof individual information to individual health quality vectors,generating a set of user data regarding the user, and deriving thedefault vector from the training set as a function of the set of userdata using a K-nearest neighbors algorithm.

At step 540, the at least a server 104 generates a plurality ofinterventions, wherein each intervention of the plurality ofinterventions includes an intervention vector having a plurality ofintervention vector entries, the plurality of intervention vectorentries includes a vector entry corresponding to each health vectorentry of the plurality of health vector entries, and each interventionvector entry indicates a degree of impact on a factor represented by ahealth vector entry; this may be implemented, without limitation, asdescribed above in reference to FIGS. 1-4.

At step 545, at least a server 104 selecting, by the at least a server104, an intervention from the plurality of interventions, whereselecting the intervention includes generating a loss function of theplurality of interventions and the user health quality vector 156,minimizing the loss function, and selecting an intervention from theplurality of interventions as a function of minimizing the lossfunction; this may be implemented, without limitation, as describedabove in reference to FIGS. 1-4.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for selecting an intervention based oneffective age, the system comprising: at least a server, the at least aserver designed and configured to: record a user blood test indicatingat least a measure of user endocrine function; generate a firstsupervised machine-learning model, wherein: the first supervisedmachine-learning model comprises a trained supervised machine-learningmodel trained by training data correlating at least a measure of userendocrine function to variances between actuarial life expectancy datumand actual mortality dates; the first supervised machine-learning modelis configured to receive the at least a measure of endocrine function asinputs; and the first supervised machine-learning model is configured tooutput a user endocrinal age factor comprising a first variance betweenactuarial life expectancy and a projected actual mortality date; recorda user genetic sample, wherein the user genetic sample includes ameasure of user telomere length; generate a second supervisedmachine-learning model, wherein: the second supervised machine-learningmodel comprises a trained supervised machine-learning model trained bytraining data correlating telomere length to variances between actuariallife expectancy datum actual mortality dates; the second supervisedmachine-learning model is configured to receive the measure of usertelomere length as a input; and the second supervised machine-learningis configured to output a user telomeric age factor model comprising asecond variance between actuarial life expectancy and a projected actualmortality date; generate a third supervised machine-learning model,wherein: the third supervised machine-learning model comprises a trainedsupervised machine-learning model trained by training data correlatinguser negative habit to variances between actuarial life expectancy datumand actual mortality dates; the third supervised machine-learning modelis configured to receive a user negative habit as an input; and thethird supervised machine-learning model is configured to output a usernegative habit factor comprising a variance between actuarial lifeexpectancy and projected actual mortality date; calculate at least auser effective age as a function of the first supervisedmachine-learning model, the second supervised machine-learning model,and the third supervised machine-learning model, wherein calculating theat least a user effective age further comprises multiplying a userchronological age by the user telomeric age factor, the user endocrinalage factor, and the user negative habit factor; derive a user healthquality vector, wherein the user health quality vector further comprisesa plurality of health vector entries including: an effective agereduction value indicating a degree of importance of effective agereduction; and at least a life quality objective value indicating anumerical measure of a user life quality priority; generate a pluralityof interventions, wherein each intervention of the plurality ofinterventions includes an intervention vector having a plurality ofintervention vector entries; the plurality of intervention vectorentries includes a vector entry corresponding to each health vectorentry of the plurality of health vector entries; and each interventionvector entry indicates a degree of impact on a factor represented by ahealth vector entry; and select an intervention from the plurality ofinterventions, wherein selecting the intervention further comprises:generating a cost function of the plurality of interventions and theuser health quality vector; minimizing the cost function; and selectingthe intervention from the plurality of interventions as a function ofminimizing the cost function.
 2. The system of claim 1, wherein the atleast a server is further configured to: receive training datacorrelating one or more changes in endocrinal measures to variancesbetween actuarial life expectancy datum and actual mortality dates;generate, using a supervised machine-learning process, an endocrinal agefactor model that receives one or more changes in endocrinal measures asinputs and produces an output representing a variance between actuariallife expectancy and a projected actual mortality date; calculate atleast a change in an endocrinal measure using the at least a measure ofendocrine function; and determine the user endocrinal age factor usingthe at least a change in the endocrine measure and the endocrinal agefactor model.
 3. The system of claim 1, wherein the at least a server isfurther configured to: identify, using a first unsupervised machinelearning process, a correlation between at least a biomarker andtelomere length; receive training data correlating the at last abiomarker to variances between actuarial life expectancy datum andactual mortality dates; and generate the training data correlatingtelomere length to variances between actuarial life expectancy datum andactual mortality dates using the training data correlating the at last abiomarker to variances between actuarial life expectancy datum andactual mortality dates and the correlation of between the at least abiomarker and telomere length.
 4. The system of claim 1, wherein the atleast a server is further configured to: receive a training setcorrelating blood tests to negative habits; generate a negative habitidentifier model using a supervised machine-learning algorithm and saidtraining set; produce a negative habit output from the negative habitidentifier model using a user blood test; and identify the user negativehabit as a function of the negative habit output.
 5. The system of claim1, wherein deriving the user health quality vector further comprises:generating a default vector; displaying the default vector to the user;receiving a user command modifying the default vector; and deriving theuser health quality vector using the default vector and the usercommand.
 6. The system of claim 5, wherein generating the default vectorfurther comprises: receiving a training set correlating a cohort ofindividual information to individual health quality vectors; generatinga set of user data regarding the user; and deriving the default vectorfrom the training set as a function of the set of user data using aK-nearest neighbors algorithm.
 7. The system of claim 1, wherein theselected intervention comprises a reduction in consumption of aparticular nutrient.
 8. The system of claim 1, wherein the selectedintervention comprises a specific meal.
 9. A method of selecting anintervention based on effective age, the method comprising: recording,by at least a server, a user blood test indicating at least a measure ofuser endocrine function; generating, by the at least a server, a firstsupervised machine-learning model, wherein: the first supervisedmachine-learning model comprises a trained supervised machine-learningmodel trained by training data correlating at least a measure of userendocrine function to variances between actuarial life expectancy datumand actual mortality dates; the first supervised machine-learning modelis configured to receive the at least a measure of endocrine function asinputs; and the first supervised machine-learning model is configured tooutput a user endocrinal age factor comprising a first variance betweenactuarial life expectancy and a projected actual mortality date;recording, by the at least a server, a user genetic sample, wherein theuser genetic sample includes a measure of user telomere length;generating, by the at least a server, a second supervisedmachine-learning model, wherein: the second supervised machine-learningmodel comprises a trained supervised machine-learning model trained bytraining data correlating telomere length to variances between actuariallife expectancy datum actual mortality dates; the second supervisedmachine-learning model is configured to receive the measure of usertelomere length as a input; and the second supervised machine-learningis configured to output a user telomeric age factor model comprising asecond variance between actuarial life expectancy and a projected actualmortality date; generating, by the at least a server, a third supervisedmachine-learning model, wherein: the third supervised machine-learningmodel comprises a trained supervised machine-learning model trained bytraining data correlating user negative habit to variances betweenactuarial life expectancy datum and actual mortality dates; the thirdsupervised machine-learning model is configured to receive a usernegative habit as an input; and the third supervised machine-learningmodel is configured to output a user negative habit factor comprising avariance between actuarial life expectancy and projected actualmortality date; calculating, by the at least a server, at least a usereffective age as a function of the first supervised machine-learningmodel, the second supervised machine-learning model, and the thirdsupervised machine-learning model, wherein calculating the at least auser effective age further comprises multiplying a user chronologicalage by the user telomeric age factor, the user endocrinal age factor,and the user negative habit factor; deriving, by the at least a server,a user health quality vector, wherein the user health quality vectorfurther comprises a plurality of health vector entries including: aneffective age reduction value indicating a degree of importance ofeffective age reduction; and at least a life quality objective valueindicating a numerical measure of a user life quality priority;generating, by the at least a server, a plurality of interventions,wherein each intervention of the plurality of interventions includes anintervention vector having a plurality of intervention vector entries;the plurality of intervention vector entries includes a vector entrycorresponding to each health vector entry of the plurality of healthvector entries; and each intervention vector entry indicates a degree ofimpact on a factor represented by a health vector entry; and selecting,by the at least a server, an intervention from the plurality ofinterventions, wherein selecting the intervention further comprises:generating a cost function of the plurality of interventions and theuser health quality vector; minimizing the cost function; and selectingthe intervention from the plurality of interventions as a function ofminimizing the cost function.
 10. The method of claim 9, furthercomprising: receiving training data correlating one or more changes inendocrinal measures to variances between actuarial life expectancy datumand actual mortality dates; generating, using a supervisedmachine-learning process, an endocrinal age factor model that receivesone or more changes in endocrinal measures as inputs and produces anoutput representing a variance between actuarial life expectancy and aprojected actual mortality date; calculating at least a change in anendocrinal measure using the at least a measure of endocrine function;and determining the user endocrinal age factor using the at least achange in the endocrine measure and the endocrinal age factor model. 11.The method of claim 9 further comprising: identifying, using a firstunsupervised machine learning process, a correlation between at least abiomarker and telomere length; receiving training data correlating theat last a biomarker to variances between actuarial life expectancy datumand actual mortality dates; and generating the training data correlatingtelomere length to variances between actuarial life expectancy datum andactual mortality dates using the training data correlating the at last abiomarker to variances between actuarial life expectancy datum andactual mortality dates and the correlation of between the at least abiomarker and telomere length.
 12. The method of claim 9, furthercomprising: receiving a training set correlating blood tests to negativehabits; generating a negative habit identifier model using a supervisedmachine-learning algorithm and the training set; producing a negativehabit output from the negative habit identifier model using a user bloodtest; and identifying the user negative habit as a function of thenegative habit output.
 13. The method of claim 9, wherein deriving theuser health quality vector further comprises: generating a defaultvector; displaying the default vector to the user; receiving a usercommand modifying the default vector; and deriving the user healthquality vector using the default vector and the user command.
 14. Themethod of claim 13, wherein generating the default vector furthercomprises: receiving a training set correlating a cohort of individualinformation to individual health quality vectors; generating a set ofuser data regarding the user; and deriving the default vector from thetraining set as a function of the set of user data using a K-nearestneighbors algorithm.
 15. The method of claim 9, wherein the selectedintervention comprises a specific exercise.
 16. The method of claim 9,wherein the selected intervention comprises a number of sleep hours.